Invited review: Phenotyping strategies and quantitative-genetic background of resistance, tolerance and resilience associated traits in dairy cattle

König, Sven; May, Katharina

doi:10.1017/s1751731118003208

Cited by 55 publications

(47 citation statements)

References 66 publications

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“…One of the most significant advances in dairy cattle health and welfare in recent decades has been the shift from treatment of clinical diseases to the implementation of preventive strategies to maintain health (LeBlanc et al ). Moreover, significant research efforts are now centered on exploring genetic variation associated with the ability of animals to control disease (König & May ).…”

Section: Resultsmentioning

confidence: 99%

Genome‐enable prediction for health traits using high‐density SNP panel in US Holstein cattle

et al. 2020

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Summary The objective of this study was to compare accuracies of different Bayesian regression models in predicting molecular breeding values for health traits in Holstein cattle. The dataset was composed of 2505 records reporting the occurrence of retained fetal membranes (RFM), metritis (MET), mastitis (MAST), displaced abomasum (DA), lameness (LS), clinical endometritis (CE), respiratory disease (RD), dystocia (DYST) and subclinical ketosis (SCK) in Holstein cows, collected between 2012 and 2014 in 16 dairies located across the US. Cows were genotyped with the Illumina BovineHD (HD, 777K). The quality controls for SNP genotypes were HWE P‐value of at least 1 × 10−10; MAF greater than 0.01 and call rate greater than 0.95. The FImpute program was used for imputation of missing SNP markers. The effect of each SNP was estimated using the Bayesian Ridge Regression (BRR), Bayes A, Bayes B and Bayes Cπ methods. The prediction quality was assessed by the area under the curve, the prediction mean square error and the correlation between genomic breeding value and the observed phenotype, using a leave‐one‐out cross‐validation technique that avoids iterative cross‐validation. The highest accuracies of predictions achieved were: RFM [Bayes B (0.34)], MET [BRR (0.36)], MAST [Bayes B (0.55), DA [Bayes Cπ (0.26)], LS [Bayes A (0.12)], CE [Bayes A (0.32)], RD [Bayes Cπ (0.23)], DYST [Bayes A (0.35)] and SCK [Bayes Cπ (0.38)] models. Except for DA, LS and RD, the predictive abilities were similar between the methods. A strong relationship between the predictive ability and the heritability of the trait was observed, where traits with higher heritability achieved higher accuracy and lower bias when compared with those with low heritability. Overall, it has been shown that a high‐density SNP panel can be used successfully to predict genomic breeding values of health traits in Holstein cattle and that the model of choice will depend mostly on the genetic architecture of the trait.

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Section: Resultsmentioning

confidence: 99%

Genome‐enable prediction for health traits using high‐density SNP panel in US Holstein cattle

et al. 2020

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“…Low heritability might only indicate high phenotypic variability in comparison to the total additive genetic variance. Therefore, when necessary, it is crucial to identify alternative variables that can better capture the genetic variability for the trait(s) of interest (i.e., higher heritability; König and May, 2019). The rate of genetic progress for a certain trait also depends on the generation interval (Falconer and Mackay, 1996), and therefore, traits that are measured earlier in life, but reflect the welfare status of the animal in its whole life (or at a later stage), are desirable.…”

Section: Main Requirements For Identifying Welfare Traits For Selectimentioning

confidence: 99%

Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding

et al. 2020

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Genomic breeding programs have been paramount in improving the rates of genetic progress of productive efficiency traits in livestock. Such improvement has been accompanied by the intensification of production systems, use of a wider range of precision technologies in routine management practices, and high-throughput phenotyping. Simultaneously, a greater public awareness of animal welfare has influenced livestock producers to place more emphasis on welfare relative to production traits. Therefore, management practices and breeding technologies in livestock have been developed in recent years to enhance animal welfare. In particular, genomic selection can be used to improve livestock social behavior, resilience to disease and other stress factors, and ease habituation to production system changes. The main requirements for including novel behavioral and welfare traits in genomic breeding schemes are: (1) to identify traits that represent the biological mechanisms of the industry breeding goals; (2) the availability of individual phenotypic records measured on a large number of animals (ideally with genomic information); (3) the derived traits are heritable, biologically meaningful, repeatable, and (ideally) not highly correlated with other traits already included in the selection indexes; and (4) genomic information is available for a large number of individuals (or genetically close individuals) with phenotypic records. In this review, we (1) describe a potential route for development of novel welfare indicator traits (using ideal phenotypes) for both genetic and genomic selection schemes; (2) summarize key indicator variables of livestock behavior and welfare, including a detailed assessment of thermal stress in livestock; (3) describe the primary statistical and bioinformatic methods available for large-scale data analyses of animal welfare; and (4) identify major advancements, challenges, and opportunities to generate high-throughput and large-scale datasets to enable genetic and genomic selection for improved welfare in livestock. A wide variety of novel welfare indicator traits can be derived from information captured by modern technology such as sensors, automatic feeding systems, milking robots, activity monitors, video cameras, and

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“…This is important to better understand the relationships between morphology, health, and genetics. Furthermore, for animal breeding purposes, detailed 3D models could be used to develop relevant indicators for traits that are not possible to measure on selection candidates, such as longevity and robustness [61][62][63]. By including relevant precision phenotypes in multivariate genomic predictions, the accuracy of estimated breeding values for economically important traits will increase [5].…”

Section: Further Use Of 3d Animal Modelsmentioning

confidence: 99%

Modelling the shape of the pig scapula

Nordbø¹

2020

Genet Sel Evol

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Background: The shape of pig scapula is complex and is important for sow robustness and health. To better understand the relationship between 3D shape of the scapula and functional traits, it is necessary to build a model that explains most of the morphological variation between animals. This requires point correspondence, i.e. a map that explains which points represent the same piece of tissue among individuals. The objective of this study was to further develop an automated computational pipeline for the segmentation of computed tomography (CT) scans to incorporate 3D modelling of the scapula, and to develop a genetic prediction model for 3D morphology. Results: The surface voxels of the scapula were identified on 2143 CT-scanned pigs, and point correspondence was established by predicting the coordinates of 1234 semi-landmarks on each animal, using the coherent point drift algorithm. A subsequent principal component analysis showed that the first 10 principal components covered more than 80% of the total variation in 3D shape of the scapula. Using principal component scores as phenotypes in a genetic model, estimates of heritability ranged from 0.4 to 0.8 (with standard errors from 0.07 to 0.08). To validate the entire computational pipeline, a statistical model was trained to predict scapula shape based on marker genotype data. The mean prediction reliability averaged over the whole scapula was equal to 0.18 (standard deviation = 0.05) with a higher reliability in convex than in concave regions. Conclusions: Estimates of heritability of the principal components were high and indicated that the computational pipeline that processes CT data to principal component phenotypes was associated with little error. Furthermore, we showed that it is possible to predict the 3D shape of scapula based on marker genotype data. Taken together, these results show that the proposed computational pipeline closes the gap between a point cloud representing the shape of an animal and its underlying genetic components.

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Invited review: Phenotyping strategies and quantitative-genetic background of resistance, tolerance and resilience associated traits in dairy cattle

Cited by 55 publications

References 66 publications

Genome‐enable prediction for health traits using high‐density SNP panel in US Holstein cattle

Genome‐enable prediction for health traits using high‐density SNP panel in US Holstein cattle

Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding

Modelling the shape of the pig scapula

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